Sensory hypersensitivities are common and distressing features of Fragile X Syndrome (FXS). While there are many drug interventions that reduce behavioral deficits in Fmr1 mice and efforts to translate these preclinical breakthroughs into clinical trials for FXS, evidence-based clinical interventions are almost non-existent potentially due to lack of valid neural biomarkers. Local circuit function in sensory networks is dependent on the dynamic balance of activity in inhibitory/excitatory synapses. Studies are needed to examine the association of electrophysiological alterations in neural systems with sensory and other clinical features of FXS to establish their clinical relevance. Adolescents and adults with FXS (n = 38, Mean age = 25.5, std = 10.1; 13 females) and age matched typically developing controls (n = 40, Mean age = 27.7, std = 12.1; 17 females) completed auditory chirp and auditory habituation tasks while undergoing dense array electroencephalography (EEG). Amplitude, latency, and percent change (habituation) in N1 and P2 event-related potential (ERP) components were characterized for the habituation task; time-frequency calculations using Morlet wavelets characterized phase-locking and single trial power for the habituation and chirp tasks. FXS patients showed increased amplitude but some evidence for reduced habituation of the N1 ERP, and reduced phase-locking in the low and high gamma frequency range and increased low gamma power to the chirp stimulus. FXS showed increased theta power in both tasks. While the habituation finding was weaker than previously found, the remaining findings replicate our previous work in a new sample of patients with FXS. Females showed less deficit in the chirp task but not the habituation task. Abnormal increases in gamma power were related to more severe behavioral and psychiatric features as well as reductions in neurocognitive abilities. Replicating electrophysiological deficits in a new group of patients using different EEG equipment at a new data collection site with differing levels of environmental noise that were robust to data processing techniques utilizing multiple researchers, indicates a potential for scalability to multi-site clinical trials. Given the robust replicability, relevance to clinical measures, and preclinical evidence for sensitivity of these EEG measures to pharmacological intervention, the observed abnormalities may provide novel translational markers of target engagement and potentially outcome measures in large-scale studies evaluating new treatments targeting neural hyperexcitability in FXS.
Previous studies have found alterations in 40 Hz oscillatory activity in response to auditory stimuli in adults with Autism Spectrum Disorder (ASD). The current study sought to examine the specificity and developmental trajectory of these findings by driving the cortex to oscillate at a range of frequencies in both children and adults with and without ASD. Fifteen participants with ASD (3 female, aged 6–23 years) and 15 age-matched controls (4 female, aged 6–25 years) underwent dense-array EEG as they listened to a carrier tone amplitude-modulated by a sinusoid linearly increasing in frequency from 0–100 Hz over 2 s. EEG data were analyzed for inter-trial phase coherence (ITPC) and single-trial power (STP). Older participants with ASD displayed significantly decreased ability to phase-lock to the stimulus in the low gamma frequency range relative to their typically developing (TD) counterparts, while younger ASD and TD did not significantly differ from each other. An interaction between age and diagnosis suggested that TD and ASD also show different developmental trajectories for low gamma power; TD showed a significant decrease in low gamma power with age, while ASD did not. Regardless of age, increased low gamma STP was significantly correlated with increased clinical scores for repetitive behaviors in the ASD group, particularly insistence on sameness. This study contributes to a growing body of evidence supporting alterations in auditory processing in ASD. Older ASD participants showed more pronounced low gamma deficits than younger participants, suggesting an altered developmental trajectory for neural activity contributing to auditory processing deficits that may also be more broadly clinically relevant. Future studies are needed employing a longitudinal approach to confirm findings of this cross-sectional study.
This paper describes a mathematical model of the learning process suitable for studies of conditioning using the proboscis extension reflex (PER) in honey bees when bees are exposed to agrochemicals. Although procedural variations exist in the way laboratories use the PER paradigm, proboscis conditioning is widely used to investigate the influence of pesticides and repellents on honey bee learning. Despite the availability of several mathematical models of the learning process, no attempts have been made to apply a mathematical model to the learning curve in honey bees exposed to agrochemicals. Our model is based on the standard transfer function in the form Y = B3e−B2 (X−1) + B4 (1−e−B2 (X−1)) where X is the trial number, Y is the proportion of correct responses, B2 is the learning rate, B3 is readiness to learn, and B4 is ability to learn. We reanalyze previously published data on the effect of several classes of agrochemicals including: (1) those that are considered harmless to bees (e.g., pymetrozine, essential oils, dicofol); (2) sublethal exposure to pesticides known to harm honey bees (e.g., coumaphos, cyfluthrin, fluvalinate, permethrin); and (3) putative repellents of honey bees (e.g., butyric acid, citronella). The model revealed additional effects not detected with standard statistical tests of significance.
IntroductionFragile X Syndrome (FXS) is rare genetic condition characterized by a repeat expansion (CGG) in the Fragile X messenger ribonucleoprotein 1 (FMR1) gene where individuals with greater than 200 repeats are defined as full mutation. FXS clinical presentation often includes intellectual disability, and autism-like symptoms, including anxiety and sensory hypersensitivities. Individuals with 55 to <200 CGG repeats are said to have the FMR1 premutation, which is not associated with primary characteristics of the full mutation, but with an increased risk for anxiety, depression, and other affective conditions, as well as and impaired cognitive processing differences that vary in severity. Defining subgroups of premutation carriers based on distinct biological features may identify subgroups with varying levels of psychiatric, cognitive, and behavioral alterations.MethodsThe current pilot study utilized 3 cluster subgroupings defined by previous k means cluster analysis on neuropsychiatric, cognitive, and resting EEG variables in order to examine basic sensory auditory chirp task-based EEG parameters from 33 females with the FMR1 premutation (ages 17–78).ResultsBased on the predefined, neuropsychiatric three-cluster solution, premutation carriers with increased neuropsychiatric features and higher CGG repeat counts (cluster 1) showed decreased stimulus onset response, similar to previous ERP findings across a number of psychiatric disorders but opposite to findings in individuals with full mutation FXS. Premutation carriers with increased executive dysfunction and resting gamma power (cluster 2) exhibited decreased gamma phase locking to a chirp stimulus, similar to individuals with full mutation FXS. Cluster 3 members, who were relatively unaffected by psychiatric or cognitive symptoms, showed the most normative task-based EEG metrics.DiscussionOur findings suggest a spectrum of sensory processing characteristics present in subgroups of premutation carriers that have been previously understudied due to lack of overall group differences. Our findings also further validate the pre-defined clinical subgroups by supporting links between disturbances in well-defined neural pathways and behavioral alterations that may be informative for identifying the mechanisms supporting specific risk factors and divergent therapeutic needs in individuals with the FMR1 premutation.
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